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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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, modeling and Remote-sensing to Transform carbon budgets, CLARiTy’ (https://www.schmidtsciences.org/vicc/) will reduce the persistently high land flux uncertainties in GCB by an order of magnitude. To achieve
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, multidisciplinary team environment. Preferred Qualifications: Knowledge of uncertainty quantification methods and causal inference for complex environmental systems. Experience with large-scale Earth system
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or willingness to learn quickly. Publications, thesis work, or demonstrable projects in computer vision, multi-modal ML, digital twins or biomedical ML. Familiarity with uncertainty quantification and model
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networks, risk analysis or uncertainty quantification (preferred). Knowledge of data science in general as well as practical experience with conducting data science analyses with good programming skills
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methods for LLMs, and (iii) develop a benchmark for uncertainty quantification in LLM-based scientific agents. The project will be carried out under the supervision of Martin Trapp and Hossein Azizpour, and
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of quantum computing concepts, quantum algorithms, or quantum-inspired methods Experience with hybrid quantum–classical algorithms or optimization methods Background in uncertainty quantification, reduced
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directions within geometric learning and uncertainty quantification will also be available for you to explore. This PhD position offers a unique chance to influence the trajectory of our group's work and